Parallel computing based parameter auto-tuning algorithm for optimization solvers

CHEN Weifeng; CHEN Jie; SHAO Zhijiang

doi:10.3969/j.issn.0438-1157.2013.12.027

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Parallel computing based parameter auto-tuning algorithm for optimization solvers

更新时间：2025-12-10

- Parallel computing based parameter auto-tuning algorithm for optimization solvers
- CIESC Journal Issue 12, Pages: 4446-4453(2013)
- 作者机构：
  
  1. 浙江大学工业控制技术国家重点实验室, 工业控制研究所,浙江,杭州,310027
  2. 浙江工业大学信息工程学院,浙江,杭州,310023
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.027
  CLC： Q021.8
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
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CHEN Weifeng, CHEN Jie, SHAO Zhijiang. Parallel computing based parameter auto-tuning algorithm for optimization solvers[J]. CIESC Journal, 2013, (12): 4446-4453.
DOI：

CHEN Weifeng, CHEN Jie, SHAO Zhijiang. Parallel computing based parameter auto-tuning algorithm for optimization solvers[J]. CIESC Journal, 2013, (12): 4446-4453. DOI： 10.3969/j.issn.0438-1157.2013.12.027.

摘要

优化求解器的参数设置对优化求解器的性能有着至关重要的影响，可以通过参数整定来充分发挥优化求解器潜在的求解性能。过程模型复杂程度的提高和规模的增大，给参数自动整定算法的效率带来极大的影响。根据基于随机采样的参数自动整定算法对每一组参数设置的选择和评估是相互独立的特点，采用并行化方法对其进行了改进。数值实验表明基于并行计算的参数自动整定能够提高整定效率，并且在足够的硬件条件支持下适合应用于面向生产过程的操作运行的在线操作优化。

Abstract

Parameter setting plays an important role in the performance of optimization solver.Hence

the potential solving performance can be full employed by tuning the parameters.The increase of complexity and scale of process model has a great influence on the efficiency of parameter auto-tuning algorithm.In this paper

according to the independence of the parameter setting selection and evaluation of the random sampling based parameter auto-tuning algorithm

the efficiency is improved by utilizing parellel technology. The numerical experiment shows that the parallel computing based parameter auto-tuning algorithm has an enhanced tuning efficiency and it is suitable to online operation optimization for operation run of production process with sufficient hardware support.

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